Intuitionistic time series fuzzy inference system. (June 2019)
- Record Type:
- Journal Article
- Title:
- Intuitionistic time series fuzzy inference system. (June 2019)
- Main Title:
- Intuitionistic time series fuzzy inference system
- Authors:
- Egrioglu, Erol
Bas, Eren
Yolcu, Ozge Cagcag
Yolcu, Ufuk - Abstract:
- Abstract: Although adaptive network fuzzy inference system and fuzzy functions approach can be utilized as a prediction tool, they have been not designed for prediction problem and they ignore the dependency structure of time series observations. From this viewpoint, making a design of the method that considers the dependency structure of observations will provide to get more accurate prediction. In this study, an intuitionistic time series fuzzy inference system (I-TSFIS) has been proposed. In the I-TSFIS, in just the same way as in the intuitionistic fuzzy inference systems, not only the membership values and crisp observations but also the non-membership values are used as inputs. Moreover, due to the use of crisp values as targets and outputs, the output does not need to be deffuzzified. Non-linear relationships between inputs and outputs of the proposed I-TSFIS are determined by Sigma-Pi neural network (SP-NN). The obtaining of optimal weights of SP-NN is performed by modified particle swarm optimization. And also I-TSFIS uses intuitionistic fuzzy C-means to obtain fuzzy clusters, membership and non-membership values of observations for these clusters. To evaluate the prediction performance of the proposed I-TSFIS, various real-life time series data sets have been analyzed and the results demonstrate the superior prediction ability of the proposed I-TSFIS. Highlights: An IS with dependency structure of observations can ensure successful prediction. We have proposed anAbstract: Although adaptive network fuzzy inference system and fuzzy functions approach can be utilized as a prediction tool, they have been not designed for prediction problem and they ignore the dependency structure of time series observations. From this viewpoint, making a design of the method that considers the dependency structure of observations will provide to get more accurate prediction. In this study, an intuitionistic time series fuzzy inference system (I-TSFIS) has been proposed. In the I-TSFIS, in just the same way as in the intuitionistic fuzzy inference systems, not only the membership values and crisp observations but also the non-membership values are used as inputs. Moreover, due to the use of crisp values as targets and outputs, the output does not need to be deffuzzified. Non-linear relationships between inputs and outputs of the proposed I-TSFIS are determined by Sigma-Pi neural network (SP-NN). The obtaining of optimal weights of SP-NN is performed by modified particle swarm optimization. And also I-TSFIS uses intuitionistic fuzzy C-means to obtain fuzzy clusters, membership and non-membership values of observations for these clusters. To evaluate the prediction performance of the proposed I-TSFIS, various real-life time series data sets have been analyzed and the results demonstrate the superior prediction ability of the proposed I-TSFIS. Highlights: An IS with dependency structure of observations can ensure successful prediction. We have proposed an intuitionistic time series fuzzy inference system (I-TSFIS). I-TSFIS, as inputs, uses the membership, the non-membership and the crisp values. I-TSFIS forms non-linear relationships between inputs and outputs via SP-NN. The training of SP-NN is performed by modified particle swarm optimization. … (more)
- Is Part Of:
- Engineering applications of artificial intelligence. Volume 82(2019)
- Journal:
- Engineering applications of artificial intelligence
- Issue:
- Volume 82(2019)
- Issue Display:
- Volume 82, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 82
- Issue:
- 2019
- Issue Sort Value:
- 2019-0082-2019-0000
- Page Start:
- 175
- Page End:
- 183
- Publication Date:
- 2019-06
- Subjects:
- Fuzzy inference system -- Intuitionistic fuzzy c-means -- Sigma-pi neural network -- Time series prediction -- Particle swarm optimization
Engineering -- Data processing -- Periodicals
Artificial intelligence -- Periodicals
Expert systems (Computer science) -- Periodicals
Ingénierie -- Informatique -- Périodiques
Intelligence artificielle -- Périodiques
Systèmes experts (Informatique) -- Périodiques
Artificial intelligence
Engineering -- Data processing
Expert systems (Computer science)
Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09521976 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.engappai.2019.03.024 ↗
- Languages:
- English
- ISSNs:
- 0952-1976
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3755.704500
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- 10911.xml